IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Feature Engineering: A Practical Guide with Examples
Feature engineering, measured step by step on 17,379 hours of real bike-rental data: how encoding the hour, adding an hour-by-working-day interaction and two domain features cut a linear model's error from 131 to 73 rentals, why the same features hurt a tree model, and the leakage rules every new feature must pass.
Supervised vs Unsupervised Learning: A Clear Comparison
Supervised versus unsupervised learning, shown on the same real data: a classifier that learns from labels, a clustering that finds the same groups without them, why scaling decided whether it could, how to judge clusters when no labels exist, and how a few labels plus unlabelled data can beat labels alone.
How to Read a Machine Learning Research Paper
How to read a machine learning research paper without drowning in it: the three-pass method applied to the Transformer paper, the questions to ask of every results table, the difference between what a paper shows and what it suspects, and a small NumPy re-implementation that checks one of its claims.
SQL for Data Science: Queries Every Data Scientist Must Know
The SQL queries data scientists write every week, each run on a real sample database with its output: joining and aggregating, the join fan-out that inflated revenue ninefold, finding what is missing with an anti-join, CTEs and window functions for running totals and top-N per group, and counting around NULLs.
Statistics for Data Science: The 20% You Actually Need
The statistics data scientists use constantly, each idea shown on real data in Python: when the median beats the mean, confidence intervals by bootstrapping, what a p-value does and does not say, why effect sizes matter, how testing many things manufactures findings, and how Simpson's paradox reverses a conclusion.
Computer Vision Basics: From Pixels to Predictions
Computer vision from the ground up: what an image is to a computer, how a convolution filter finds edges, a small convolutional network trained on handwritten digits against a pixel baseline, the main vision tasks from classification to segmentation, and why transfer learning is the default.